{"id":"W2039470741","doi":"10.1118/1.3613482","title":"TH‐A‐220‐04: MVCT Noise Reduction and Feature Enhancement for Target Delineation","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre","funders":"","keywords":"Tomotherapy; Contouring; Contrast-to-noise ratio; Feature (linguistics); Nuclear medicine; Medicine; Standard deviation; Artificial intelligence; Computer science; Mathematics; Image quality; Radiation therapy; Radiology; Image (mathematics); Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005124899,0.0003199052,0.0002042596,0.0003574178,0.0001087145,0.0002790691,0.0002831031,0.0004246072,0.001516048],"category_scores_gemma":[0.00119114,0.0001461194,0.0002558654,0.0001775327,0.0001836262,0.0001923863,0.0001866456,0.0002510965,0.000543335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002575682,"about_ca_system_score_gemma":0.0002924892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079533,"about_ca_topic_score_gemma":0.001021454,"domain_scores_codex":[0.9998253,0.00003420054,0.000009585847,0.00003633892,0.00008105553,0.00001359449],"domain_scores_gemma":[0.9997575,0.00007582606,0.00003665548,0.00003510365,0.00007170643,0.00002313727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008508487,0.0001850677,0.006388481,0.0002234718,0.00005590722,0.0001707291,0.00006322009,0.006167988,0.8516141,0.0002037687,0.0005388035,0.1335377],"study_design_scores_gemma":[0.0001169828,0.002444563,0.05641992,0.00002770829,0.0001820725,0.00383606,0.00003179969,0.089614,0.8395573,0.0001686624,0.007558367,0.00004252794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8474794,0.00129349,0.1463673,0.0001617636,0.00004766902,0.0001773174,0.0001780872,0.0009222583,0.003372684],"genre_scores_gemma":[0.8435749,0.0003472567,0.1520348,0.00007998208,0.00004186373,0.00008702165,0.000375988,0.0002103757,0.003247913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001516048,"threshold_uncertainty_score":0.0050717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768190281218959,"score_gpt":0.2933248571359975,"score_spread":0.2756429543238079,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}